Why do we need multiple LSTM units in a layer?
Let's write down Fibonacci!


  K = 0 1 1 2 3 5 ...


And another series that is derived from Fibo; 


  X = 1 4 7 12 30 $X_{5}$


Guessing $X_{5}$ is our task and both series are available to you (Fibonacci as an additional feature). 

One unit that you feed with X will try to capture the relation of $X_{t}$ and $X_{t-1}$ only;

$X_{t}$ = $X_{t-1}$ + $X_{t-2}$

However, an additional unit that you insert&amp;feed with K will not only try to capture $K_{t}$ and $K_{t-1}$ relation but also the relation between $K_{t}$ and $X_{t}$.

$K_{t}$ = $K_{t-1}$ + $2*$$X_{t}$ + $1$ 

In the example above, there is a clear correlation between $K_{t}$ and $X_{t}$ (which is not always the case) and it will support the network to capture the sequential relation. Even the CORRELATION is not crystal clear, almost every additional feature data correlates with the other features and will support the network to grab the relation.